International Competitiveness of the Mediterranean Quartet ... · Mediterranean Quartet: ......

WP/08/240

International Competitiveness of the Mediterranean Quartet:

A Heterogeneous-Product Approach

Herman Z. Bennett and Ziga Zarnic

© 2008 International Monetary Fund WP/08/240 IMF Working Paper European Department

International Competitiveness of the Mediterranean Quartet: A Heterogeneous-Product Approach∗

Prepared by Herman Z. Bennett and Ziga Zarnic

Authorized for distribution by James Daniel

October 2008

Abstract

This Working Paper should not be reported as representing the views of the IMF. The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate.

The real effective exchange rate (REER) is the most commonly used measure for assessing international competitiveness. We develop a methodology to estimate the REER thatincorporates two distinctive elements that are not considered in the current literature and apply it to the Mediterranean Quartet (MQ) of Greece, Italy, Portugal, and Spain, whose common pattern of real appreciation has created concern in policy and academic circles. The two elements that we add to the existing literature are (i) product heterogeneity when identifying each country's international competitors and their weights and (ii) a comprehensive treatment of services exports. Our refined measure suggests a modest reduction in the observed REER gap between the MQ countries and the other euro area countries. In particular, considering productheterogeneity and services exports implies a lower real appreciation from 1998 to 2006 on the order of 2-3 percent for all MQ countries. These are difference-in-difference estimates relative to the results obtained for the rest of the euro area countries using the same methodology.

JEL Classification Numbers: F10, F30

Keywords: International competitiveness, real effective exchange rate, product heterogeneity, industry level, structure of competitors, services exports, euro area.

Author’s E-Mail Address: [email protected], [email protected]

∗ We are grateful to James Daniel, Jan De Loecker, Michael Deppler, Julio Escolano, Eva Gutierrez, Alex Hoffmaister, Deniz Igan, Emilia Jurzyk, Bogdan Lissovolik, Stephen Tokarick, and seminar participants at IMF, LICOS-Katholieke Universiteit Leuven, and Université Catholique de Louvain for valuable comments and discussions. Arlene Tayas's and Zhaogang Qiao's effective help providing the necessary editorial assistance for this working paper version is very much appreciated. All errors are our own. The opinions presented should not be attributed to the International Monetary Fund, its Executive Board, or its management. Ziga Zarnic also gratefully acknowledges the hospitality of IMF's European Department where much of this work was done during the summer of 2007. Comments welcome at [email protected], and [email protected].

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Contents Page I. Introduction ..............................................................................................................................4 II. Methodology ...........................................................................................................................7 A. A Generic Approach to Aggregate Relative Cost Competitiveness ...........................7 B. Global Goods, Local Consumption of Local Production, RPA, and Aggregated Cost Measures .........................................................................................9 III. Data ........................................................................................................................................10 A. Goods and Services.....................................................................................................10 B. Global Goods...............................................................................................................12 IV. Results....................................................................................................................................13 A. The Effect of the Heterogeneous-Product Approach and Services.............................14 B. Differentiated ULC by Sector .....................................................................................17 V. The Profile of International Competitors ................................................................................18 A. Goods ..........................................................................................................................18 B. Services .......................................................................................................................19 VI. Conclusion .............................................................................................................................19 Figures 1. Real Appreciation in the MQ vs. the Rest of the Euro Area, 1998–2006................................21 2. Importance of China and Germany as Competitors of the MQ in High-Tech and Low-Tech Sectors in Goods, 1998-2005...................................................................................................22 Tables 1. Literature Overview.................................................................................................................23 2. ULC-Based REER Indices, Goods, 1998–2006: Heterogeneous-Product Approach (G) and Representative-Product Approach (1G)...................................................................................24 3. Net Appreciation Differential Since 1998: Marginal Effect of Heterogeneous-Product Approach (G vs. 1G)................................................................................................................24 4. Net Appreciation Differential Since 1998: Robustness Check 1 (1G vs. IMF).......................25 5. Net Appreciation Differential Since 1998: Marginal Effect of Extended Sample (G vs. G27)..................................................................................................................25 6. Net Appreciation Differential Since 1998: Marginal Effect of Domestic Market Competition (G vs. GDM) .......................................................................................................26 7. The Structure of Competitors: Heterogeneous- vs. Representative-Product Approach (λ) and vs. Partners Approach (λp) .....................................................................................................26 8. ULC-Based REER Indices, Services, 1998–2006: Services Heterogeneous-Product

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Approach (S)............................................................................................................................27 9. Net Appreciation Differential Since 1998: Difference Between Services and Goods (S vs. G) .................................................................................................................27 10. ULC-Based REER Indices, Goods and Services, 1998-2006: A Heterogeneous-Product Approach (GS) .......................................................................................................................28 11. Net Appreciation Differential Since 1998: Joint Marginal Effect of the Heterogeneous- Product Approach, Including Services (GS vs. 1G) ..............................................................28 12. Net Appreciation Differential Since 1998: Marginal Effect of Differentiated ULC (Gd vs G).......................................................................................................................29 13. Structure of Competitors: Goods ...........................................................................................30 14. Structure of Competitors in High-Tech and Low-Tech Sectors in 2005 ...............................30 15. Structure of Competitors: Services ........................................................................................30 16. Main Competitors in 2005: Goods.........................................................................................31 17. Main Competitors in 2004: Services......................................................................................31 Appendix Tables A1. Availability of Unit Labor Cost Data....................................................................................32 A2. Classification of Global Goods .............................................................................................33 References....................................................................................................................................34

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I. Introduction

In this paper, we develop a methodology to estimate the real effective exchange rate (REER) that incor-porates two distinctive elements not accounted for in the current literature: (i) product heterogeneity whendetermining international competitors and their weights, which allows us to identify countries’ direct inter-national competitors more accurately, and (ii) a comprehensive treatment of services exports, which allowsus to provide a complete view of international competitiveness encompassing the entire export sector.

We apply this methodology to reexamine the evolution of the REER of the Mediterranean Quartet (MQ)of Greece, Italy, Portugal, and Spain, and particularly, the evolution of their REER gap with the other euroarea members. This case motivates our analysis as the common pattern of real appreciation observed in theMQ countries has created concern in policy and academic circles (Bini-Smaghi 2007, EC 2006, Roubini2007, Papademos 2007, The Economist 2007). Particular attention has been given to the fact that this patterndiverges from the average real depreciation observed in the rest of the euro area (see Figure 1). It is arguedthat this real appreciation is associated with a loss of international competitiveness in the MQ and that itcould lead to a persistent period of slow growth, which has already materialized in the cases of Portugal andItaly (Blanchard 2006a and 2006b).1

In short, the REER is an aggregated measure of cost competitiveness between countries. It tracks the evolu-tion of cost competitiveness of a particular country with respect to a weighted average of all other countriesin the world.2 The methodologies available to calculate the REER have been constantly improving in re-cent decades as they have been incorporating more realistic assumptions. Table 1 summarizes the existingliterature and highlights the approach taken in each study to address the key elements of the REER analysis,that is, the approach used to calculate the importance or weight of each other country and the price usedto measure cost competitiveness. Bayoumi et al. (2006 and 2005) is the most comprehensive methodologycurrently available, which includes the latest development in the literature.

Determining the weights by identifying the degree to which countries compete in international markets, asopposed to weighting by trade partnership, is one of the most important characteristics that distinguishes themost up-to-date REER estimations. To illustrate the importance of this feature, consider the extreme case oftwo countries, A and B, that export mostly to a third country C and have nil bilateral trade between them. Ifthe weight of country B in the calculation of country A’s REER is based on trade partnership, then changesin the exchange rate of country B will not alter the REER of country A. This is not a desirable feature of anindex of relative cost competitiveness, since countries A and B compete when exporting to country C and

1For example, The Economist stated in early 2007 that “In particular, the Mediterranean quartet of Italy, Spain,Portugal and Greece has suffered a huge loss of competitiveness in a relatively short time...This loss is reflected incolossal current-account deficits (...) or pitifully slow growth (...).” (The Economist 2007)

2 See Agenor (1995), Catao (2007), Chinn (2006), Fung and Klau (2006), Marsh and Tokarick (1996), Neary(2006), and Rogoff (1996) for further references to the concept of REER.

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exchange rate movements in either country clearly affect the relative cost competitiveness of the other one.The interrelationship between the cost competitiveness of countries A and of country B is better captured bythe REER if the weights are based on a measure of how much these two countries compete in internationalmarkets.

With respect to the method used to identify international competitors and their weights, the existing literatureconsiders that two countries are international competitors if they both sell products in the same country, thatis, in a market defined as a single aggregated sector comprising a representative product category—which werefer to as the “representative-product approach”, henceforth RPA. As a result, the RPA assumes implicitlythat all exporters compete with each other in the destination country. In contrast, we take a more micro-based approach that considers product heterogeneity when defining markets and identifying internationalcompetitors and their weights. For each product type that we consider, we identify international competitorsas competitors competing in the market for that product type in the destination country. This allows us to an-alyze relative cost competitiveness at disaggregated markets according to the type of product and destinationcountry. We aggregate these market-level REER indices to obtain a country-level REER—which we referto as the “heterogeneous-product approach”, henceforth HPA. In principle, our methodology can be appliedto alternative definitions of market. Based on data availability, however, we define markets at 4-digit ISICcategory of goods and at 2-digit ISIC category of services. 3

The HPA identifies more precisely a country’s direct international competitors, and thus, their weights. Thisfeature operates at two levels: first, with respect to other exporters, and second, with respect to local producersat the destination of exports. To illustrate the differences, assume that country A exports textiles to countryC, while country B exports cars to country C. The RPA focuses on competitors at an aggregate level—at themanufacturing sector for example, the most common case in the literature—suggesting that countries A andB compete in market C, even though exporters of cars are not necessarily competitors of textile exporters.Furthermore, the RPA would imply that all manufacturing goods produced in country C are competitors ofexporters to country C, regardless of the type of good that is produced in, and exported to, country C. Incontrast, the disaggregated view of the HPA would not consider countries A and B as competitors in thisexample and would consider only textile producers in country C as competitors of country A.

3We initially attempted to base our definition of international competitors on the degree of substitution betweengoods. We intended to include producers of other 4-digit level industries as competitors, weighting their importanceby the degree of substitution between the corresponding goods, as measured by the cross elasticity of substitution.Available volume indices, however, present important measurement error. This affects the estimation of price indices,necessary for the estimation of the cross elasticities, and therefore, the whole methodology would have resulted in asignificant increase in the variance of the REER estimates. Also, the presence of monopolistic competition at differ-ent intensities across industries and countries suggests that the estimation of cross elasticities is subject to potentialidentification problems. We have, therefore, assumed the simplifying assumption that the structure of internationalcompetitors within 4-digit sectors provide a good representation of the structure of competitors that exporters withinthose sectors face. The same is assumed for services within the 2-digit industry-level.

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With respect to services exports, our approach provides a comprehensive view of relative cost competitive-ness by incorporating information about exports of services as well as exports of goods. Services exportshave become increasingly important and represent 65 percent of total exports in Greece, 19 percent in Italy,27 percent in Portugal, and 31 percent in Spain. As with the case of goods, we identify competitors in thedestination market at disaggregated categories of services. Unfortunately, the available data on disaggregatedbilateral trade in services are not as complete as the data for trade in goods, and therefore, our estimates ofthe REER in services are restricted to the available sample of trade flows. The average coverage of bilateraltrade ranges from 89 percent of total services exports for Greece to 59 percent for Spain. The coverage forgoods is above 90 percent for all MQ countries.

Our results suggest a modest reduction in the observed REER gap between the MQ countries and the othermembers of the euro area. Allowing for product heterogeneity (HPA) and services exports implies, comparedwith the standard results obtained under the RPA, a lower real appreciation from 1998 to 2006 on the order of2-3 percent for all MQ countries—2.0 percent for Greece, 2.8 percent for Italy, 2.4 percent for Portugal, and2.3 percent for Spain. These figures are based on difference-in-difference estimates that control for the resultsobtained for the rest of the euro area countries using the same methodology. As a robustness check, we alsoshow that our results obtained under the RPA are consistent with the ones reported using the methodologypresented in Bayoumi et al. (2006 and 2005), the closest methodology to the one presented in this paper thatuses RPA.

The above results are based on a single cost measure at the country-level as used in the current literature,namely the unit labor cost (ULC). To the extent that wages and productivity growth vary across exportingsectors, differentiated cost measures at the sector-level would yield a more accurate picture of internationalcompetitiveness. We explore this avenue and compute the REER using differentiated ULC measures at the2-digit level. Unfortunately, the sample of countries with differentiated ULC series (28) is more restrictedthan the sample with aggregated ULC series (38), particularly regarding Asian countries. Also, the availabletime span is one year shorter than in the aggregate ULC data. Moreover, the ULC series at the industry-levelmay be more volatile because of its disaggregated nature, which could be magnifying some of the knownproblems of the ULC indicator as a measure of cost competitiveness (see footnote 2). Therefore, the resultsbased on differentiated ULC should be taken with caution, given the data limitations detailed above, andshould be read as an exploratory effort to determine the effect of differentiated cost measures on the REER.

The micro-based methodology proposed in this study also allows for a quantitative assessment of each coun-try’s profile of competitors. Such evidence provides information about the exposure of each country to itskey competitors around the world; for example, the exposure to emerging competitors like China, whichhas shown a strong pattern of productivity and trade growth, or the exposure to countries facing significantchanges in their cost structure, such as the wage moderation observed recently in Germany and the depre-ciation of the nominal exchange rate observed in the USA during the recent years. Our findings indicatethat the bulk of competition for the MQ still comes from the advanced economies, especially from the euro

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area—Spain and Portugal are more exposed to euro area competition, and therefore, less exposed to changesin the value of the euro. Nonetheless, there has been a change in the goods sector, as emerging economieshave grown in importance since the late 1990s, particularly China in both high- and low-technology sectors.

The remainder of the paper is organized as follows. Section II presents the methodology used to compute theREER under the HPA. Section III describes the data used in the estimations. Section IV presents the mainestimates of the REER, which account for product heterogeneity and services trade, and reports robustnesschecks. Section V presents the main results regarding the profile of competitors. Section VI concludes.

II. Methodology

This section presents the methodology used to estimate the evolution of the REER under the HPA. The firstsubsection develops a generic framework to aggregate at the country-level the relative cost competitivenessdynamics observed at the country-industry-level. This framework allows us to incorporate into the analy-sis elements such as global goods—goods whose markets are defined at the world-level rather than at thecountry-level—and local consumption of local production—the competition that local producers represent atexports’ destination.

A. A Generic Approach to Aggregate Relative Cost Competitiveness

We construct our index of relative cost competitiveness between country i and the rest of the countries in theworld (ROW), denoted by Ri,t , using a geometrical Laspeyres index and the chain link methodology. Thesubscripts g, d, and t refer to the type of product (including both goods and services), destination (country),and time (year), respectively.

The evolution of the Ri,t index is defined in equation 1, where T denotes the base year, and ∆Ωi,t denotesthe natural logarithmic change of the relative cost competitiveness between country i and the ROW betweenperiod t and t−1.4

lnRi,t = lnRi,T +t

∑j=T+1

∆Ωi, j (1)

The change in relative cost competitiveness at the country-level, ∆Ωi,t , is constructed as the weighted average

4Throughout the paper, the notation ∆ denotes the natural logarithmic difference between t and t−1 of the corre-sponding variable.

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of the change in the relative cost competitiveness between country i and the ROW in each market definedby the g,d pair (equation 2). The change in the relative cost competitiveness in market g,d is denoted by∆θ i,g,d,t . The weights are given by the importance of each market g,d in country i’s exports and are denotedby βi,g,d,t−1. The term βi,g,d,t−1 is computed as the share of country i’s total exports represented by its exportsof product g to destination d (equation 3).

∆Ωi,t = ∑∀(g,d)

βi,g,d,t−1 ·∆θ i,g,d,t (2)

βi,g,d,t−1 =Si,g,d,t−1

∑∀(g,d)

Si,g,d,t−1(3)

Where Sc,g,d,t represents sales by country c of product g in destination d; i.e. sales by country c in marketg,d.

The change in relative cost competitiveness at the market-level, ∆θ i,g,d,t , is given by equation 4—defined ala IMF, that is, a higher number means more appreciated. It is constructed as the difference between countryi’s cost change and a weighted average of the same cost change observed in all other countries competing inmarket g,d. The variables Pi,g,t and Pc,g,t represent the cost variable used to estimate cost competitiveness,are specific to each industry in each country, and are expressed in the local currency. The variable Ei,c,t

represents the exchange rate between country i and country c defined as units of country i’s currency per unitof country c’s currency.

∆θ i,g,d,t = ∆Pi,g,t − ∑∀c6=i

αi,c,g,d,t−1 · (∆Pc,g,t +∆Ec,i,t) (4)

The weight αi,c,g,d,t−1 is given by the importance of each country (c) as a competitor of country i in marketg,d. We relate this weight to the market participation (share) of country c in market g,d, denoted by γc,g,d,t .

γc,g,d,t−1 =Sc,g,d,t−1

∑∀c

Sc,g,d,t−1(5)

Defining αi,c,g,d,t−1 = γc,g,d,t−1, however, implies that the sum of the weights of all competitors is less thanone, i.e. ∑∀(c 6=i,g,d) βi,g,d,t−1 ·αi,c,g,d,t−1 < 1, because country i is not considered as a competitor of itselfin equation 4. Alternatively, one could add country i in the latter sum to make it equal to one. However,doing so would violate an important property that an estimator of the REER should have: ceteris paribus,if all competitors of country i depreciate their currency by 10 percent, then, country i’s REER appreciates

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by 10 percent—the 10 percent property, for short. This property is violated if country i is added to thesummation because the total mass of all countries excluding country i is less than one. Another alternativewould be to exclude country i when computing each country’s market share γc,g,d,t−1. However, this solutioncreates a bias, overstating the importance of small competitors relative to the importance of big competitors.To illustrate this point, assume a foreign market with two equally large competitors plus an exporter fromcountry i. Excluding country i from the computation of the market share would imply that the other twocompetitors would represent 50 percent of the market, regardless of their actual importance as competitorsof country i in that market.

We propose an alternative methodology to measure the importance of each country in each market as a com-petitor of country i, that is, βi,g,d,t−1 ·αi,c,g,d,t−1. We rescale the importance of each competitor in each marketbased on the relative importance that this competitor has in that market vis-a-vis the importance of all othercompetitors in all other markets (equation 6). Our adjusted measure satisfies the condition that the weightsof all competitors sum up to one, that is ∑∀(c6=i,g,d) βi,g,d,t−1 ·αi,c,g,d,t−1 = 1, satisfies the 10 percent property,and does not over or understate the relative importance of each competitor vis-a-vis all other competitors ofcountry i.

βi,g,d,t−1 ·αi,c,g,d,t−1 =βi,g,d,t−1 · γc,g,d,t−1

∑∀(c 6=i,g,d)

βi,g,d,t−1 · γc,g,d,t−1(6)

B. Global Goods, Local Consumption of Local Production, RPA, andAggregated Cost Measures

The more comprehensive REER estimates available in the current literature incorporate two important ele-ments into the analysis (see Table 1 for details): global goods and local consumption of local production.The former refers to goods that can be characterized as commodities (e.g. copper) and for which a moreappropriate definition of market is at the world-level rather than at the country-level. Regarding consumptionof local production, this refers to the competition that local producers represent at exports’ destination, andit is proxied by the difference between local production of a good and the exports of that good from thatdestination to the rest of the world.

Our generic approach can be used to consider these two elements. First, we define an artificial additionaldestination d that will not correspond to a particular country but to the world. Therefore, all goods g that areconsidered global goods (see next section for details) are assumed to compete in the market (g,d=world).Second, we incorporate local consumption of local production by defining the case d=c, which refers tocompetitor c competing in the market g,d=c.

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We also estimate the REER under the RPA to study the marginal effect of the HPA. The methodology pre-sented in the previous section can easily consider this case as well by redefining all goods into a single goodg = g. Following Bayoumi et al. (2006 and 2005), we treat global goods separately under the RPA.

Finally, the lack of data limits the extent to which differentiated cost measures by sector can be modeled. Thegeneric approach can be adjusted to consider aggregated measures for the corresponding subsectors withinthe defined aggregation level by simply defining ∆Pc,g,t = ∆Pc,g,t ∀g s.t. g ⊂ g , where g refers to anaggregated sector.

III. Data

A. Goods and Services

Bilateral trade data for goods are compiled from the United Nations Commodity Trade Statistics Database(COMTRADE). The data include 144 different activity classes of goods (4-digit ISIC Rev.3) across 200countries over the period 1998-2005. Bilateral trade data for services are compiled from the OECD Statisticsof International Trade in Services. The data include 9 categories of services, according to the ExtendedBalance of Payments Services Classification (EBOPS), across 100 countries over the period 1999-2004. Thestructure of competitors in 1998 and 2005 is extrapolated from the information available for 1999 and 2004,respectively. The average coverage of bilateral trade data for the MQ ranges from 86 percent of total exportsof services for Greece to 59 percent for Spain. The same figures for goods are all above 90 percent.

Disaggregated local production series are needed to estimate local consumption of local production. Ob-taining consistent and complete production data at 4-digit level is a challenging endeavor because availabledatabases present significant differences in product and time coverage across countries. We approach thisdifficulty by combining various databases and generating (rough) estimates where possible. Our main sourceis the United Nations Industrial Demand-Supply Balance database (IDSB), which contains data at the 4-digitlevel of ISIC Rev.3 classification, which comprises 127 manufacturing commodities and 78 countries. Weextend the IDSB database using (i) the annual growth rates of output reported in Eurostat’s Annual EnterpriseStatistics database (4-digit NACE Rev.1.1 production data);5 (ii) the observed ratio between sectoral outputand aggregate manufacturing output in Eurostat’s Annual Enterprise Statistics database; (iii) the observedgrowth rate of output production in total manufacturing to extend the series for a maximum window of threeyears—if output production growth in total manufacturing is not available, we use value added growth in to-

5Eurostat’s Annual Enterprise Statistics database has good coverage of production data, but includes only membersof European Union. With respect to the correspondence used, we consider data that (i) corresponds to only one type ofproduct at the 4-digit ISIC Rev.3 level and (ii) does not share the image with data points that correspond to more thanone type of product at the 4-digit ISIC Rev.3 level.

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tal manufacturing. Finally, we consider only series with complete data for the period 1998 to 2005 (originalor estimated) to avoid biases/changes in the REER measures due to truncation of series unrelated to changesin relative cost competitiveness.

With respect to production of services, we use the EU KLEMS Growth and Productivity Accounts database.EU KLEMS database reports data for EU25 countries, Australia, Japan, and the USA until 2005. Productiondata for royalties and license fees are not considered because the match between EBOPS and ISIC Rev.3classifications has many shared codes that makes it impossible to build a consistent correspondence. Timecoverage differs across country and sectors, although to a much lower extent than in the case for goods.We extend the series in the same fashion as we do for goods, using production data from the OECD-STANdatabase and GDP growth rates. We consider only series with complete time series data (original or esti-mated).

The coverage for local production of goods, defined as the share of exports represented by the destinationmarkets for which we can construct data on local consumption of local production, ranges from 50 percentfor Greece to 70 percent for Portugal (60 percent for Italy and 66 percent for Spain). The coverage forlocal production of services, defined similarly, is 85 percent for Greece, 70 percent for Italy, 83 percent forPortugal, and 94 percent for Spain. These last figures are not necessarily comparable with the figures forlocal production of goods because their computation is based on the available bilateral trade data, which hasa lower coverage for services than for goods.

We focus on REER measures that proxy cost competitiveness using the ULC, as opposed to consumer andproducer price indices (CPI and PPI, respectively). The latter variables have the advantage of being availablefor most countries around the world. However, the ULC measure seems to be more appropriate because itconsiders changes in productivity. The ULC measure allows us to incorporate, albeit not perfectly, importantdynamics when considering cost competitiveness, such as the Balassa-Samuelson effect and the effect ofinnovation and structural reforms across countries.6

Manufacturing ULC is used as the aggregate ULC measure at the country-level. The data are obtaineddirectly from the OECD Analytic Database and WEO database and are available for 38 countries. Industry-level ULC data at 2-digit level is computed using the EU KLEMS database, which covers 28 countries until2005. Table A1 in Appendix details the different samples. The industry-level ULC is computed as theratio of the compensation of employees per hour worked to real gross value added per hour worked. Thecompensation of employees per hour worked is obtained from the ratio of compensation of employees tototal hours worked by employees.

6See Lipschitz and McDonald (1992), Marsh and Tokarick (1996), Agenor (1995), Turner and Van t’dack (1993),and Cerra, Soikkeli, and Saxena (2003) for more on the advantages and disadvantages of using ULC as a measure ofcost competitiveness.

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Most of the disaggregated ULC series are complete for the countries covered by the EU KLEMS database,although data for 2006 are not available. Disaggregated ULC series with incomplete data for the period1998 to 2005 are not considered and are replaced by the country’s ULC series for the manufacturing sectorcomputed from the EU KLEMS database—in order to avoid biases/changes in the REER measures due totruncation of series unrelated to changes in relative cost competitiveness.

The annual average nominal exchange rates are obtained from the IFS database.

B. Global Goods

We refer to globally traded goods as those goods whose prices are quoted on organized world exchangesas defined by Rauch (1999). Rauch (1999) classifies goods into three categories at the 4-digit SITC Rev.2.classification: commodities, reference-priced goods and differentiated goods. This classification is based onwhether a good is traded and priced on organized world exchanges, listed in trade publications but not tradedon organized exchanges, or does not posses a reference price, respectively.

In order to identify global goods within the 4-digit ISIC Rev.3 classification, we identify all the ISIC Rev.3codes associated with each SITC Rev.2 in Rauch (1999) by using the UN correspondence tables. We assigna value of 1 to each good priced in organized world exchanges and a value of 3 to each good that does notpossess a reference price. We calculate the average value of the associated codes for each ISIC Rev.3 codein a similar way to Jensen (2006). We define a good as globally traded at the 4-digit ISIC Rev.3 level if thevalue of the calculated average within each ISIC Rev.3 code lies in the interval of [1,2). Goods with valuesin the interval of [2,3] are not considered as globally traded goods.

Rauch (1999) presents two classifications: the “conservative” and the “liberal”. The liberal version maxi-mizes the number of globally traded goods in the cases where there was room for discretion in the sorting.Table A2 in Appendix presents the resulting group of goods identified as global goods at the 4-digit ISICRev.3 classification under both alternatives, conservative and liberal.

For further comparisons, we also report the goods that appear as global when applying a methodology similarto the one applied to Rauch’s list to the list of global (commodity) goods defined by Bayoumi et al. (2006 and2005) at the 2-digit SITC Rev.3 level. The resulting number of global goods from the latter source is higherthan the one resulting from our methodology, most likely because in Bayoumi et al. (2006 and 2005) the listis defined at a more aggregated category of goods than in Rauch (1999) and this paper. In our analysis, weuse Rauch’s (1999) liberal classification following Jensen (2006), which results in a list that is closer to theone implied by the methodology used in Bayoumi et al. (2006 and 2005).

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IV. Results

This section presents the sensitivity analysis of the REER to the HPA and to the inclusion of services exports.Based on the methodology described in Section II, we estimate the path of the REER indices under thealternative approaches and present comparative statistics.

We first compare the estimated path of the REER under the HPA, denoted by RG, with the equivalent REERindex under the RPA, denoted by R1G. The difference represents the effect of relaxing the assumption thatall nonglobal goods are treated as identical goods and as a result compete in the same market g,d, allowingdifferentiated goods. Second, we compare RG with the estimates obtained for the evolution of the REER thatconsiders only exports of services (under the HPA), denoted by RS. This allows us to have a perspective ofhow the REER for goods compares with the REER for services. Third, we compute the aggregated REERindex for goods and services, denoted by RGS, and we compare it with R1G. This difference represents thesensitivity of the REER under the RPA to both the HPA and a broader coverage of exports that includesservices. In addition, we perform different robustness checks.

We also study the sensitivity of the REER for goods to heterogeneous cost dynamics across sectors. Wecompare the estimated REER for goods under the HPA and the heterogeneous cost dynamics assumption,denoted by RGd , where d stands for differentiated cost measures, with RG. As detailed in the introduction,these results should be taken with caution because of data limitations and should be read as an exploratoryeffort to determine the effect of differentiated sectoral cost measures on the REER.

The contrast between different approaches, for example, the comparison between RG and R1G, is performedin two dimensions. First, we present the difference observed in the appreciation rates from 1998 to thecorresponding year shown in the tables for both indices. Using 1998 as the base year for comparison is anadhoc rule, which has no other merit than being year prior to the adoption of the euro adoption by all the euroarea countries (January 1, 1999), except for Greece (January 1, 2001). This difference in levels is computedfollowing equation 7.

Difference in levels (Level) = ∆t%RGi −∆t%R1G

i (7)

Where ∆t% refers to the growth rate of the index observed from 1998 to year t and i refers to the countrywhose REER is analyzed.

Second, we study the difference observed between the two estimations of the REER considered, for ex-ample between RG and R1G, but consider each estimator relative to the corresponding REER observed inthe remaining 11 countries of the euro area. This difference-in-difference estimator is computed followingequation 8.

14

Difference in difference (DD) =[∆t%RG

i −∆t%RGEA

]−

[∆t%R1G

i −∆t%R1GEA

](8)

Where ∆t% refers to the growth rate of the index observed from 1998 to year t, i refers to the country whoseREER is analyzed, and EA refers to the remaining 11 countries of the euro area.7

The difference-in-difference estimator (DD for short) is our preferred estimator for two reasons. First, itallows us to control for methodological issues specific to each type of estimation that could be driving theresults without necessarily reflecting changes in relative cost competitiveness. Second, it allows us to con-trol for the equivalent results observed in the rest of 11 euro area countries. Therefore, the DD estimatorrepresents the change in the relative international competitiveness position between the euro area countries.This does not mean that the DD estimator considers only direct competitors from the euro area, but that itcompares among euro area countries each country’s position with respect to direct competitors across theworld.

The euro area countries are all part of the same currency union, and therefore, are a natural benchmark tocompare the evolution of the REER in each of the MQ countries and control for potential methodologicaldifferences particular to each type of estimation. This approach has also an important economic meaning.The exchange rates between euro area countries are fixed—although the euro is still sensitive to the inter-national competitiveness of the euro area as a whole, in line with the standard exchange rate mechanismsassociated with floating currencies. No rebalancing through nominal exchange rate movements is then pos-sible, but only through productivity and wage growth differentials, which tend to take longer to materialize.As a result, divergence in international competitiveness between euro area countries is an important elementwhen assessing the medium-term economic perspective of individual euro area countries.

A. The Effect of the Heterogeneous-Product Approach and Services

A.1 REER for goods under the HPA

Table 2 presents the estimation of the REER indices RG and R1G for the MQ countries, alongside the esti-mations for the two main euro area countries, France and Germany, for further comparison. The contrastbetween RG and R1G is reported in Table 3. The figures for the DD estimator imply that under the HPAPortugal’s, Italy’s, and Spain’s REERs are less appreciated in the range of 2 percent in 2006 (1998 base).The difference is larger in the case of Greece, on the order of 7 percent. This indicates that, relative to the

7 The index RGEA is computed as a geometrical index of the individual RG indices for each of the remaining 11

countries. We construct the weights based on their relative size of exports of the particular type of product considered(goods, services, or goods and services).

15

effect on the other 11 euro area countries, the REER under the RPA in Greece is 7 percent more appreciatedthan what the model assuming the HPA suggests (since 1998).

Robustness checks

We study the robustness of our methodology and results by performing three additional contrasts. First, wecompare our computation of R1G with the closest measure available in the literature (based on Bayoumi etal. 2006 and 2005); second, we modify the sample of countries considered; and third, we study how ourmeasure of REER changes when domestic market competition is considered.

Table 4 presents the comparison between the R1G and RIMF , where IMF stands for the WEO estimates of theREER based on the methodology proposed by Bayoumi et al. (2006 and 2005)—the closest source to ourmethodology that includes the latest developments in the literature and uses the RPA. The results for the DDestimator are all within the ±1 percent range, suggesting that our methodology under the RPA yields similarresults to the existing methodologies based on the RPA.8

Our sample of countries is based on the available information for ULC in manufacturing in OECD and theWEO databases (Table A1 in Appendix). This sample differs from the sample used to compute RIMF , whichconsiders 27 countries. An interesting aspect of the additional 11 countries used in our sample is that theyconstitute a sample of emerging countries not represented in the sample of 27 advanced countries, with theexception of China.9

We performed a second robustness check to study if our estimates of RG are sensible to including the addi-tional 11 emerging countries. We contrast RG with the REER estimated under the HPA with the list of 27countries, denoted by RG27. The results, presented in Table 5, indicate that the REER estimated with the sam-ple of 27 countries does not differ substantially from the REER estimated with the sample of 38 countries.The results for the DD estimator are all within the ±1 percent range.

Finally, we consider the potential importance of domestic market competition for measuring internationalcompetitiveness. Due to a lack of consistent data on disaggregated internal production across MQ countries,our analysis centers on the external markets where each MQ country competes. Table 6 presents the results ofcontrasting RG with the REER under HPA including the available information on internal markets, denotedby RGDM. It indicates that the marginal effect of domestic markets is small with differences in the range of±1 percent.10

8Our estimates as well as the IMF estimates reported include ULC data as of August 2007.9The sample of 27 countries covers on average 70-85 percent of MQ’s competitors, while the sample of 38 countries

covers 90-80 percent.10We cannot perform this analysis for Greece because of insufficient data on its disaggregated structure of produc-

tion.

16

Difference in the structure of competitors

We complement the results on the effect of the HPA presented in Table 3 with an aggregate view of thedifference in the structure of competitors implied by the HPA and the RPA. The larger the difference, thegreater the likelihood of finding a large difference between the corresponding REER measures. The actualeffect, however, will depend on the interaction between the different weights and the distribution of thechange in ULC across countries. In the limit, even large differences in the weights will have no effect if allcountries present identical changes in their ULCs, and vice versa, even small differences in the weights canhave large effects if changes in ULC are significantly different across countries.

We capture the difference in the structure of competitors implied by each approach using the formula de-scribed in equation 9. The variable λi,t aggregates the difference observed in the weight assigned to eachcompetitor of country i in period t.

λi,t = ∑∀c 6=i

∣∣∣χi,c,t −χ1gi,c,t

∣∣∣ (9)

Where χi,c,t is defined by equation 10 and βi,g,d,t ·αc,g,d,t refers to the relative importance of each competitorc in market (g,d) with respect to all other competitors in all other markets.11 The variable χ1g

i,c,t refers to thecalculation under the RPA.

χi,c,t = ∑∀(g,d)

βi,g,d,t ·αc,g,d,t , where ∑∀c 6=i

χi,c,t = 1 (10)

Table 7 presents the results for λi,t . Greece, whose sensitivity to the HPA is the highest among all countries,presents the highest level of difference. This was expected, although as mentioned above, the fact that adifference is observed in λi,t does not necessarily imply a difference in the REER measure; it only makes itmore likely. In fact, among the rest of the countries, Portugal stands out with the largest value for λi,t , butdoes not present the highest sensitivity to the HPA.

Table 7 also presents the difference in the structure of competitors when comparing the HPA and the partners-approach, denoted by λ p. The partners-approach refers to considering a country’s trade partners as its com-petitors and it is used by some sources; see Chinn (2006) for more details. The results show that consideringthe partners-approach yields a stronger difference, two to three times the size of λi,t , when comparing it tothe HPA. This difference is expected since considering competitors only on the base of trade partnershipsdeviates substantially from the concept of competitors used in this paper.

11See section 2 for more details

17

A.2 REER for goods and services under the HPA

Table 8 presents the estimation of the REER index RS, which includes only services exports. To illustrate theevolution of the services component, we compare RS and RG in Table 9. These results suggest that except forthe case of Greece, the services component of the REER has appreciated less than the goods component forthe MQ countries. This difference ranges from -3.4 percent for Italy to -0.9 percent for Spain (DD estimator).For Greece, the difference goes in the opposite direction in the range of 7 percent (DD estimator).

Finally, Table 10 presents the estimation of the REER index RGS, which includes both goods and services.We compare RGS with R1G in Table 11, which represents the aggregate sensitivity of the REER under theRPA to both the HPA and a broader coverage of exports that includes services.

The results suggest that these two additional factors together—HPa and services—have had a marginal effecton the REER on the order of -2 percent to -3 percent for all the MQ countries: -2 percent for Greece, -2.3 percent for Spain, -2.4 percent for Portugal and -2.8 percent for Italy. These numbers are consistentwith the previous tables, where the smaller appreciation observed in goods for Italy, Portugal and Spainunder the HPA adds to the smaller appreciation observed in services relative to goods. For the case ofGreece, the strongest difference observed under the HPA shrinks significantly when combined with the largerappreciation observed in services relative to goods.

B. Differentiated ULC by Sector

Product heterogeneity (HPA) and services exports refine the REER as a measure of international competitive-ness, but, as detailed in the previous section, these two factors do not change substantially the broad pictureof international competitiveness in the MQ. In this section, we explore the sensitivity of the REER to theHPA with differentiated cost measures at the sector-level. Differentiated cost measures would yield a moreaccurate picture of international competitiveness to the extent that productivity and production costs varyacross sectors. The set of results presented in this section, which indicate a higher sensitivity of the REERrelative to the assumption of homogenous cost dynamics, should, however, be taken with caution. Given thedata limitations detailed above, these results should be read as an exploratory effort to determine the effectof differentiated sectoral cost measures on the REER.

The results for the contrast between the REER estimated with an aggregated ULC measure (RG) and theREER estimated with a differentiated ULC by sector (RGd) point to a higher sensitivity of the REER to theassumption of homogenous cost dynamics across sectors—both measures based on the sample for goods be-cause of the excessive volatility found in the data for service. As shown in Table 12, the absolute differencesrange between 2 percent and 6 percent. For this contrast, RG is computed using the limited data set availablefor the calculation of RGd .

18

These results are not sensitive to outliers. We recalculated the REER eliminating the 0.5 percent tails of thedistribution of the annual ULC growth rates observed since 1998. No substantial differences from the resultsobtained in Table 12 were found. As an additional robustness check, we compared the results obtained forthe contrast between RG and R1G (Table 3) with an equivalent contrast using the limited data set available forthe calculation of RGd . The differences between both cases are all within the ±1 percent range.

V. The Profile of International Competitors

The HPA proposed in our study also allows a quantitative assessment of each country’s profile of competitors.Such evidence provides information about the exposure of each country to its key competitors around theworld—for example, the exposure to emerging competitors like China, a country that has shown a strongpattern of productivity and trade growth, or the exposure to countries facing significant changes in their coststructure, such as the wage moderation observed recently in Germany or the depreciation of the nominalexchange rate observed in the USA during recent years. Our definition of markets also captures the potentialvulnerability of each country’s sectors to changing market conditions in competitors’ sectors beyond thecountry-level.

A. Goods

For all six countries, the bulk of competition comes from the advanced and emerging economies, representingon average 95 percent in goods (except for Greece, 92 percent) and 98 percent in services. Since the late1990s, there has been a change in the composition with emerging economies taking greater importance: theyrepresented in 2005 14 percent of overall exposure to competition in goods for Spain, 19 percent for Italyand Portugal, and 22 percent for Greece (Table 13). China appears as the largest competitor in goods for allcountries, representing at least half of the increase in the importance of emerging economies.

Among the advanced economies, the euro area countries represent 59 percent of the competition in goodsfaced by Spain and Portugal, 49 percent for Italy, and 47 percent for Greece. These data indicate that Spainand Portugal are more exposed to euro area competition and therefore less exposed to changes in the valueof the euro. There has been a declining trend since 1998 in the range of 1 percentage point for all countries,which is smaller than the change observed for the aggregate of advanced economies.

From a sectoral point of view, the four MQ countries compete more in low-technology sectors with China—the main emerging market competitor (see Table 14). However, the importance of China in high-technologysectors is growing as well (see Figure 2). As a comparison, France and Germany compete more stronglywith China in high-technology sectors, suggesting that China should not be seen as an important current and

19

future competitor in low-tech sectors only. 12

Table 2 and Figure 2 also show the importance of Germany—the main advanced-country competitor—as acompetitor of the MQ. The almost flat or sometimes decreasing importance of Germany highlights the stronggrowth of China’s importance in both high- and low-technology sectors. Nonetheless, at least until 2005,Germany was still a bigger competitor for the MQ than China in both types of sectors.

B. Services

In services, emerging markets represent on average about one-third of their importance in goods, showing asimilar increase in recent years although to a lesser extent (see Table 15). From 1999 to 2004, the compositionshifted to emerging economies in the range of 3 percent for Greece and Italy, 2 percent for Portugal, and 1percent for Spain. The data suggest that China does not appear as a strong competitor in services.

Among the advanced economies, the euro area countries represent 60 percent of the competition in servicesfaced by Portugal, 50 percent for Italy, 48 percent for Spain, and 37 percent for Greece. There has been a niltrend since 1998 for all countries except for Spain, whose euro area competition has declined by 5 percentagepoints. These figures for services should be read with caution, given the incomplete availability of the datafor bilateral trade of services.

Table 16 (goods) and Table 17 (services) present a list of the top 10 competitors for each country with theircorresponding weights.

VI. Conclusion

We develop a complete methodology to reexamine the evolution of international competitiveness in the MQ,as measured by the REER. In addition to the elements considered in the existing literature, we (i) use amicro-based approach that considers product heterogeneity when identifying a each country’s internationalcompetitors and their weights and (ii) include a comprehensive analysis of the services sector. Our approachenriches the REER analysis by identifying more accurately each country’s direct international competitorsand providing an aggregate view of international competitiveness that encompasses the complete exportsector.

Our main findings suggest that the effect of considering both the more micro-based structure of competitors

12Following the OECD revised classification of sectors based upon the technology used as proposed byHatzichronoglou (1997) we classify the 2-digit ISIC Rev.3 sectors as low- (L) and high- (H) technology sectors.

20

and exports of services implies a modest lower real appreciation from 1998 to 2006 on the order of 2-3percent for all MQ countries—2.0 percent for Greece, 2.8 percent for Italy, 2.4 percent for Portugal, and2.3 percent for Spain. These estimates are based on a difference-in-difference estimator that controls for theequivalent effect observed in the rest of 11 euro area countries.

Finally, the methodology proposed in this paper also allows a detailed view of the structure of each country’scompetitors. Our findings indicate that the bulk of competition for the MQ still comes from the advancedeconomies, especially from the euro area. Nonetheless, there has been a change in the composition withemerging economies taking more importance since the late 1990s, particularly China in both the high- andlow-technology sectors.

Notes: ULC-based REER index defined à la IMF (higher means more appreciated); the base year is 1998. EA-8 refers toAustria, Belgium, France, Finland, Germany, Ireland, Luxembourg, and Netherlands. The REER for the EA-8 is estimatedaggregating each countries' REER and weighting by their total exports of goods. All countries adopted the euro on January1, 1999, except for Greece, which adopted the euro on January 1, 2001. Source: OECD.

Figure 1. Real Appreciation in the MQ vs. the Rest of the Euro Area, 1998-2006

85

95

105

115

125

1998 1999 2000 2001 2002 2003 2004 2005 2006

EA-8

Italy

Greece

Spain

Portugal

21

Figure 2. Importance of China and Germany as Competitorsof the MQ in High-Tech and Low-Tech Sectors in Goods, 1998-2005

Notes: Technology intensity of sectors is defined according to OECD (2007) classification of industries withrespect to intensity of technology used. High-tech industries comprise industries with high and medium-highintensity of technology used and low-tech industries comprise industries with low and medium-low intensityof technology used. Percentage points refer to the share of competition coming from each country.

China

Germany

01

23

40

12

34

1998 2000 2002 2004 2006 1998 2000 2002 2004 2006 1998 2000 2002 2004 2006

Greece Italy Portugal

Spain France Germany

Impo

rtan

ce o

f Chi

na a

s co

mpe

titor

(%

)

05

1015

05

1015

1998 2000 2002 2004 2006 1998 2000 2002 2004 2006 1998 2000 2002 2004 2006



H_tech L_tech

Impo

rtan

ce o

f Ger

man

y as

com

petit

or (

%)

Year

22

ReferencePrice Competitiveness

Measure

Partners / competitors

Product & market definition (goods) Services Local consumption of

local production

BIS (Fung & Klau, 2006)

Competitors Representative-productapproach. All manufacturing goods (SITC Rev. 3 5-8) are treated as one identical good; nonmanufacturing goods are not considered. Markets are defined at the country level.

Services are not included in the analysis.

Total manufacturing output.

Aggregate prices. CPI and/or manufacturing ULC are used to measure relative cost competitiveness.

Bank of Japan (BoJ, 2007)

Partners Total exports of goods. Services are not included in the analysis.

Does not apply. Aggregate prices. CPI and/or manufacturing ULC are used to measure relative cost competitiveness.

ECB(Buldorini et al., 2002)

Competitors Representative-productapproach. All manufacturing goods (SITC Rev. 3 5-8) are treated as one identical good;non-manufacturing goods are not considered. Markets are defined at the country level.


Manufacturing output for domestic use.

Aggregate price. CPI,manufacturing ULC, PPI and/or wholesale prices are used to measure relative cost competitiveness.

FRB (Loretan, 2005)

Averagebetweencompetitorsand partners

Representative-productapproach. All goods are treated as one identical good (except for oil, gold, and military items, which are not considered).


Local production is not considered in the analysis.


IMF (Bayoumi et al., 2005)

Competitors Representative-productapproach. All manufacturing goods (SITC Rev. 3 5-8, excl. 68) are treated as one identical good. Commodities are disaggregated into 20 categories at 2-dig. SITC Rev.3 level. Markets are defined at the country level for manufactured goods and at the global level for commodities (global goods).

Services are considered in the analysis, but assumed to have the same trade pattern as the observed pattern for manufacturing goods. Tourism is treated separately only for a subset of countries.

Manufacturing output for domestic use.


OECD(Durand et al., 1992, 1998)

Competitors Representative-productapproach. All manufacturing goods (SITC Rev. 3 5-9) are treated as one identical good. Markets are defined at the country level for individual OECD countries and at the level of country aggregates for 6 non-OECD country groups.


Total manufacturing output.

Aggregate price. CPI and/or manufacturing ULC are used to measure relative cost competitiveness.

Bennett & Zarnic (2008)

Competitors Heterogeneous-productapproach. All goods are treated disaggregately at 4-digit ISIC Rev. 3 level. Markets are defined at the country level for all nonglobal goods and at the global level for global goods.

Services are treated disaggregately at 2-digit ISIC Rev. 3 level. Markets are defined at the country level for all services.

Total output for domestic use (at industry-level).

Aggregate and disaggregate prices. CPI, Manufacturing ULC, and sectoral ULC data for goods (1- and 2-digit ISIC Rev. 3) are used to measure relative cost competitiveness.

Table 1. Literature Overview

Importance of Other Countries (Definition of Markets)

Notes: The representative-product approach refers to the common approach used in the literature, which assumes that all (or most) exporting goods compete with each other inthe international markets. The heterogeneous-product approach refers to the approach used in this paper, which assumes that exporting goods compete with each other withindisaggregated categories of goods and of services. The following examples illustrate the difference between looking at partners, looking at competitors assuming therepresentative-product approach, and looking at competitors assuming the heterogeneous-product approach. First, note that two countries, A and B, could compete whenexporting the same good to a third country, C, while trade between countries A and B could be either high or low. This suggests that the degree of trade between countries doesnot necessarily reflect the degree to which countries compete in international markets. Second, assume that country A exports textiles to country C, while country B exports carsto country C. Focusing on competitors at an aggregate level at the manufacturing sector for example, as it is the most common case in the literature would suggest thatcountries A and B compete in market C, even though exporters of cars are not necessary relevant competitors of exporters of textiles. Furthermore, the homogeneous-productapproach would imply that all manufacturing goods produced in country C are competitors of exporters to country C, regardless of the type of good that is produced in andexported to country C. See Chinn (2006) and Fung & Klau (2006) for a more detailed exposition of the different methodologies available in the literature.

23

G 1G G 1G G 1G G 1G G 1G G 1G1998 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.001999 96.74 96.53 99.61 99.70 101.86 102.14 99.71 99.83 97.45 97.31 98.09 98.412000 89.76 89.27 94.75 94.61 99.03 99.28 98.73 98.67 91.40 91.31 90.76 91.282001 85.99 85.31 96.98 96.97 99.00 99.35 99.46 99.50 90.25 90.24 89.88 90.352002 89.44 90.03 102.12 102.50 101.47 102.00 101.58 102.03 92.41 92.41 92.63 93.022003 94.33 96.12 113.13 114.16 103.22 104.08 108.06 109.22 95.94 95.71 97.02 97.042004 107.10 112.30 120.88 122.26 102.30 103.27 112.41 113.84 97.50 96.92 97.52 97.092005 104.81 110.04 124.64 125.98 102.90 104.08 114.79 116.16 96.18 95.24 92.60 91.652006 107.91 113.90 127.96 129.21 103.09 104.21 116.58 117.77 97.48 96.35 90.31 88.99

Level DD Level DD Level DD Level DD Level DD Level DD1999 0.21 0.27 -0.09 -0.04 -0.29 -0.23 -0.12 -0.07 0.13 0.23 -0.32 -0.382000 0.49 0.61 0.13 0.28 -0.25 -0.14 0.06 0.18 0.08 0.23 -0.52 -0.602001 0.68 0.89 0.01 0.25 -0.34 -0.14 -0.04 0.18 0.00 0.25 -0.47 -0.392002 -0.59 -0.40 -0.38 -0.21 -0.53 -0.34 -0.45 -0.27 0.00 0.23 -0.39 -0.292003 -1.79 -1.64 -1.03 -0.99 -0.86 -0.71 -1.16 -1.06 0.22 0.46 -0.02 0.192004 -5.21 -5.30 -1.38 -1.64 -0.98 -1.06 -1.43 -1.59 0.58 0.61 0.43 0.542005 -5.22 -5.64 -1.34 -1.93 -1.18 -1.59 -1.37 -1.86 0.95 0.68 0.95 0.862006 -5.98 -6.66 -1.25 -2.12 -1.12 -1.78 -1.18 -1.93 1.12 0.58 1.31 1.04Notes: G and 1G refer to the ULC-based REER for goods estimated using the heterogeneous-product approach and therepresentative-product approach, respectively. "Level" denotes the difference between the growth rates of G and 1G. "DD"denotes the difference-in-difference estimate between G and 1G. Results are presented in percentages (%).

Greece

France Germany

Marginal Effect of Heterogeneous-Product Approach (G vs. 1G)

Greece Italy Portugal Spain

Note: The base year is 1998.

Table 3. Net Appreciation Differential Since 1998:

Table 2. ULC-Based REER Indices, Goods, 1998-2006:Heterogeneous-Product Approach (G) and Representative-Product Approach (1G)

Spain France GermanyItaly Portugal

24

Level DD Level DD Level DD Level DD Level DD Level DD1999 0.06 -0.27 0.13 -0.22 0.26 -0.07 0.04 -0.31 0.15 -0.21 0.51 0.272000 -0.56 -0.76 -0.40 -0.69 0.14 -0.05 -0.58 -0.83 -0.27 -0.56 0.20 0.002001 -0.60 -0.63 -0.67 -0.80 0.08 0.05 -0.91 -1.00 -0.33 -0.43 -0.05 -0.122002 0.14 -0.30 0.03 -0.47 0.57 0.13 -0.51 -1.01 0.30 -0.17 0.46 0.032003 0.53 0.17 0.51 0.17 0.87 0.51 -0.36 -0.77 0.36 0.00 0.53 0.252004 1.07 0.29 1.28 0.56 1.22 0.45 -0.01 -0.84 0.71 -0.08 1.12 0.512005 0.35 0.15 0.50 0.33 0.81 0.62 -0.67 -0.93 0.16 -0.05 0.45 0.382006 0.24 0.15 0.36 0.30 0.64 0.56 -0.82 -0.96 0.06 -0.03 0.27 0.28

Level DD Level DD Level DD Level DD Level DD Level DD1999 -0.61 -0.75 0.05 -0.10 -0.09 -0.23 0.05 -0.09 0.09 -0.07 0.28 0.212000 -1.25 -1.22 -0.19 -0.18 -0.46 -0.43 -0.19 -0.16 -0.06 -0.03 0.16 0.302001 -1.13 -0.85 -0.42 -0.16 -0.54 -0.27 -0.37 -0.09 -0.22 0.07 -0.16 0.172002 -0.99 -0.71 -0.42 -0.16 -0.42 -0.14 -0.29 -0.01 -0.21 0.08 -0.21 0.102003 -0.67 -0.57 -0.25 -0.17 -0.24 -0.14 -0.05 0.06 -0.09 0.02 -0.02 0.132004 -0.87 -0.77 -0.31 -0.24 -0.28 -0.18 -0.03 0.08 -0.10 0.01 0.01 0.172005 -1.58 -1.20 -0.83 -0.49 -0.64 -0.26 -0.42 -0.03 -0.35 0.05 -0.21 0.272006 -1.38 -1.05 -0.81 -0.54 -0.54 -0.21 -0.37 -0.04 -0.31 0.03 -0.13 0.30

Notes: 1G refers to the ULC-based REER for goods estimated using the representative-product approach and IMF refers to theULC-based REER calculated by the IMF (based on Bayoumi et al. 2005). "Level" denotes the difference between the growth ratesof 1G and IMF. "DD" denotes the difference-in-difference estimate between 1G and IMF. Results are presented inpercentages (%).

Table 4. Net Appreciation Differential Since 1998: Robustness Check 1 (1G vs. IMF)

Greece Italy Portugal Spain France Germany

Notes: G and G27 refer to the ULC-based REER for goods estimated using the heterogeneous-product approach and theheterogeneous-product approach based on a reduced sample of 27 countries, respectively. "Level" denotes the difference betweenthe growth rates of G and G27. "DD" denotes the difference-in-difference estimate between G and G27. Results are presented inpercentages (%).


France GermanyGreece Italy Portugal Spain

Marginal Effect of Extended Sample (G vs. G27)

25

Level DD Level DD Level DD Level DD Level DD Level DD1999 na na -0.26 -0.05 -0.11 0.10 -0.22 -0.01 -0.15 0.07 -0.40 -0.272000 na na -0.82 -0.07 -0.52 0.25 -0.90 -0.15 -0.99 -0.27 -1.15 -0.582001 na na -1.00 -0.31 -0.48 0.25 -1.08 -0.37 -0.95 -0.26 -0.95 -0.332002 na na -0.76 -0.38 -0.06 0.37 -0.90 -0.50 -0.48 -0.07 -0.50 -0.102003 na na -0.26 -0.40 0.83 0.76 -0.47 -0.59 0.40 0.37 0.20 0.172004 na na 0.03 -0.46 1.38 0.96 -0.20 -0.68 0.98 0.66 0.69 0.392005 na na -0.18 -0.68 1.44 1.02 -0.35 -0.83 0.99 0.67 0.77 0.522006 na na -0.26 -0.79 1.57 1.14 -0.33 -0.83 1.04 0.70 0.79 0.54

p p p p p p1999 0.134 0.309 0.054 0.170 0.105 0.196 0.070 0.186 0.051 0.203 0.063 0.2192000 0.135 0.325 0.060 0.174 0.105 0.213 0.073 0.194 0.052 0.203 0.065 0.2112001 0.132 0.347 0.062 0.179 0.106 0.213 0.071 0.202 0.056 0.206 0.065 0.2022002 0.127 0.342 0.065 0.182 0.105 0.200 0.074 0.213 0.057 0.205 0.068 0.2032003 0.125 0.319 0.065 0.190 0.102 0.200 0.074 0.206 0.058 0.210 0.067 0.2042004 0.131 0.318 0.063 0.197 0.096 0.193 0.072 0.220 0.058 0.208 0.068 0.2012005 0.132 0.310 0.065 0.189 0.099 0.197 0.070 0.222 0.062 0.196 0.068 0.198

Average 0.133 0.322 0.061 0.183 0.103 0.201 0.072 0.204 0.056 0.204 0.067 0.208Notes: Following equation 9, refers to the difference in the structure of competitors for goods when comparing theheterogeneous-product approach with the representative-product approach. Likewise, p refers to the difference in the structureof competitors when comparing the heterogeneous-product approach with the partners-approach for goods.

Table 7. The Structure of Competitors: Heterogeneous- vs. Representative-Product Approach ( ) and vs. Partners Approach ( p)


Notes: G and GIM refer to the ULC-based REER for goods estimated using the heterogeneous-product approach and theheterogeneous-product approach including domestic market competition, respectively. "Level" denotes the difference betweenthe growth rates of G and GIM. "DD" denotes the difference-in-difference estimate between G and GIM. Results are presented inpercentages (%).



Marginal Effect of Domestic Market Competition (G vs. GIM)

26

199819992000200120022003200420052006

Level DD Level DD Level DD Level DD Level DD Level DD1999 -1.13 -0.18 -0.67 0.36 -0.14 0.91 0.03 1.26 -1.11 -0.12 -0.29 0.942000 -5.05 -4.07 -1.54 -0.39 -1.44 -0.16 -0.84 0.82 -2.42 -1.42 -0.09 1.822001 -5.72 -5.28 -1.10 -0.33 -1.41 -0.50 -0.74 0.57 -2.11 -1.48 0.09 1.792002 -2.73 -1.63 -1.09 0.17 -1.21 0.18 -0.62 1.23 -2.14 -0.94 -0.43 1.632003 0.61 1.37 -0.92 -0.24 -0.42 0.50 1.18 2.79 -1.18 -0.34 -0.94 0.662004 5.97 7.01 -1.93 -1.73 -0.66 -0.01 0.57 1.93 -1.43 -0.95 -1.25 0.442005 5.96 6.40 -2.15 -2.66 -1.07 -0.99 -0.21 0.63 -2.03 -2.36 -1.32 0.572006 6.64 6.77 -2.34 -3.36 -1.70 -2.05 -1.24 -0.86 -2.29 -3.15 -1.28 0.65

Table 8. ULC-Based REER Indices, Services, 1998-2006:


Heterogeneous-Product Approach (S)

Notes: S refers to the ULC-based REER for services estimated using the heterogeneous-product approach and G refers to the ULC-based REER for goods estimated using the heterogeneous-product approach. "Level" denotes the difference between the growthrates of S and G. "DD" denotes the difference-in-difference estimate between S and G. Results are presented in percentages (%).



93.2195.88

112.21118.95122.49

94.95113.06110.78

125.62

95.6184.7180.2786.70

114.55

102.80101.64101.83101.39

101.03

99.7497.9098.73

100.97

101.7197.5997.59

100.26

98.94

109.23112.98114.58115.35

96.3388.9788.1490.2794.7696.0794.1595.19

91.2889.02

97.8090.6789.9792.20


Difference between Services and Goods (S vs. G)

100.00 100.00100.00 100.00 100.00 100.00

96.0796.28

27

199819992000200120022003200420052006

Level DD Level DD Level DD Level DD Level DD Level DD1999 -0.53 -0.30 -0.21 0.04 -0.33 -0.08 -0.12 0.15 -0.08 0.20 -0.35 -0.152000 -3.00 -2.69 -0.14 0.25 -0.66 -0.30 -0.17 0.27 -0.38 -0.03 -0.53 -0.172001 -3.29 -3.00 -0.18 0.21 -0.75 -0.37 -0.25 0.21 -0.38 -0.01 -0.46 0.002002 -2.36 -2.00 -0.57 -0.18 -0.88 -0.46 -0.63 -0.15 -0.39 0.02 -0.44 0.082003 -1.09 -0.86 -1.20 -1.07 -0.97 -0.71 -0.85 -0.53 0.06 0.38 -0.13 0.372004 -0.88 -0.87 -1.72 -2.00 -1.15 -1.20 -1.28 -1.29 0.36 0.38 0.30 0.682005 -0.88 -1.32 -1.72 -2.50 -1.46 -1.97 -1.41 -1.86 0.62 0.15 0.82 1.032006 -1.19 -1.96 -1.67 -2.82 -1.57 -2.44 -1.46 -2.27 0.75 -0.11 1.19 1.21

Table 10. ULC-Based REER Indices, Goods & Services, 1998-2006:


Heterogeneous-Product Approach (GS)

100.00 100.00 100.00 100.00

90.94 90.7596.00 99.49 101.81 99.71

100.00 100.0097.24 98.06

89.86 89.8986.27 94.4882.01 96.79 98.60 99.25

98.62 98.50

87.67 101.93 101.12 101.40

97.29 97.3995.03 112.96 103.11 108.36

92.02 92.5895.77 96.91

95.85 92.47111.42 120.54109.16 124.26 102.62 114.75

102.13 112.56

97.10 90.18112.71 127.55 102.64 116.30


Notes: GS refers to the ULC-based REER for goods and services estimated using the heterogeneous-product approach and 1Grefers to the ULC-based REER for goods estimated using the representative-product approach. "Level" denotes the differencebetween the growth rates of GS and 1G. "DD" denotes the difference-in-difference estimate between GS and 1G. Results arepresented in percentages (%).



Joint Marginal Effect of the Heterogeneous-Product Approach, Including Services (GS vs. 1G)

28

Level DD Level DD Level DD Level DD Level DD Level DD1999 0.15 -0.04 -1.29 -1.71 -2.21 -2.43 -0.09 -0.30 -0.82 -1.20 2.22 2.962000 1.61 0.95 -0.88 -1.79 -1.14 -1.83 -0.48 -1.23 -0.04 -0.85 4.00 4.862001 0.85 0.48 -0.82 -1.38 -1.17 -1.57 -1.48 -1.98 0.59 0.25 2.23 2.722002 2.57 2.05 -1.09 -1.86 -2.82 -3.40 -0.89 -1.52 0.42 -0.13 1.23 1.022003 1.83 1.24 -1.28 -2.12 -1.88 -2.51 -0.71 -1.38 1.44 1.02 0.44 -0.222004 1.32 -0.11 -1.92 -3.79 -0.95 -2.41 -2.90 -4.61 0.96 -0.57 2.38 1.412005 5.57 4.24 -3.98 -6.00 -0.64 -2.02 -3.67 -5.34 -0.30 -2.00 2.90 2.322006 na na na na na na na na na na na na

Notes: Gd and G refer to the ULC-based REER for goods estimated using the heterogeneous-product approach with differentiatedULC measures and the heterogeneous-product approach with a country-level ULC measure, respectively. "Level" denotes thedifference between the growth rates of Gd and G. "DD" denotes the difference-in-difference estimate between Gd and G. Resultsare presented in percentages (%).


Table 12. Net Appreciation Differential Since 1998:Marginal Effect of Differentiated ULC (Gd vs. G)


29


Euro area, 1998 47.5% 49.7% 60.0% 59.9% 48.9% 42.6%Euro area, 2005 47.0% 48.6% 58.6% 58.5% 48.6% 41.1%

Advanced economies, 1998 72.4% 81.4% 83.7% 85.9% 85.7% 84.5%Advanced economies, 2005 69.9% 75.7% 77.0% 81.5% 80.1% 77.4%

Emerging economies, 1998 (1) 18.6% 14.7% 12.7% 11.1% 11.7% 11.8%Emerging economies, 2005 (2) 22.0% 19.2% 19.0% 14.4% 15.9% 17.7%

Change in percentage points (2)-(1) 3.4% 4.5% 6.3% 3.3% 4.3% 5.9%Change in percentage points due to China 3.5% 3.4% 3.4% 1.8% 2.3% 2.6%

Greece Italy Portugal Spain France GermanyChina as competitor

High-tech sectors 1.43% 2.16% 2.22% 1.36% 2.12% 2.64% Low-tech sectors 4.17% 3.76% 3.15% 1.79% 1.64% 1.18%

Germany as competitor

High-tech sectors 5.01% 11.94% 7.84% 12.82% 13.74% … Low-tech sectors 6.29% 6.63% 4.61% 3.90% 4.95% …


Euro area, 1999 37.4% 49.6% 60.3% 54.4% 42.8% 46.0%Euro area, 2004 37.2% 50.2% 60.3% 48.1% 42.5% 40.7%

Advanced economies, 1999 95.9% 94.1% 97.1% 95.8% 95.1% 93.6%Advanced economies, 2004 92.0% 89.7% 94.7% 93.5% 91.1% 87.8%

Emerging economies, 1999 (1) 3.5% 4.9% 2.4% 3.5% 4.2% 5.0%Emerging economies, 2004 (2) 6.4% 7.5% 3.9% 4.7% 6.7% 8.6%

Change in % points (2)-(1) 2.9% 2.6% 1.5% 1.2% 2.5% 3.6%Change in % points due to China 0.3% 0.2% 0.1% 0.1% 0.2% 0.3%Change in % points due to top 5 EE 2.0% 1.6% 0.6% 0.5% 1.8% 2.3%

Table 15. Structure of Competitors: Services

Note: The key 5 emerging economies (EE) competitors for Greece are South Korea, Turkey, Hungary, Czech Rep., HongKong; for Italy, they are Hungary, Turkey, Czech Rep., South Korea, Hong Kong; for Portugal, they are Turkey, CzechRep., Egypt, Hungary, Mexico; for Spain, they are Turkey, Czech Rep. Egypt, Hungary, South Africa; for France, they areSouth Korea, Turkey, Hong Kong, Hungary, Czech Rep.; and for Germany, they are South Korea, Hong Kong, CzechRep., Turkey and Hungary.

Note: Percentage points refer to the share of competition coming from each country or group of countries.

Table 13. Structure of Competitors: Goods

Notes: Technology intensity of sectors is defined according to OECD (2007) classification of industries with respect tointensity of technology used in sectors producing goods. High-tech industries comprise 2-digit industries with high andmedium-high intensity of technology used and low-tech industries comprise 2-digit industries with low and medium-lowintensity of technology used. Percentage points refer to the share of competition coming from each country.

Table 14. Structure of Competitors in High-Tech and Low-Tech Sectors in 2005

30

Rank Greece Italy Portugal Spain France Germany1 Italy Germany Spain Germany Germany US

(11.84%) (18.63%) (15.76%) (16.92%) (18.82%) (13.40%)

2 Germany France Germany France US France(11.50%) (11.60%) (12.52%) (16.46%) (12.20%) (11.68%)

3 France US France Italy Italy Italy(7.13%) (9.72%) (12.20%) (9.98%) (9.62%) (9.37%)

4 US Spain Italy US UK UK(6.54%) (6.26%) (8.52%) (7.23%) (7.76%) (7.70%)

5 UK UK US UK Spain Japan(6.14%) (6.25%) (5.81%) (6.49%) (6.82%) (6.85%)

6 China China UK Belgium Japan Netherlands(5.76%) (5.95%) (5.63%) (4.29%) (4.46%) (4.78%)

7 Spain Japan China Netherlands Belgium Spain(4.51%) (4.33%) (5.41%) (4.06%) (4.24%) (4.71%)

8 Belgium Belgium Belgium Portugal Netherlands Belgium(4.15%) (3.71%) (3.65%) (3.95%) (4.10%) (4.14%)

9 Netherlands Netherlands Netherlands Japan China China(3.93%) (3.23%) (3.35%) (3.37%) (3.80%) (3.83%)

10 Turkey Austria Japan China Korea Sweden(3.59%) (2.23%) (2.41%) (3.23%) (1.96%) (2.73%)

Rank Greece Italy Portugal Spain France Germany1 US US UK UK US US

(29.57%) (15.85%) (17.73%) (26.40%) (16.20%) (15.97%)

2 UK Germany Spain Germany UK France(16.20%) (14.45%) (17.49%) (13.79%) (16.16%) (10.38%)

3 Germany France France France Italy Japan(9.60%) (13.12%) (14.53%) (11.74%) (10.38%) (10.20%)

4 Italy UK US US Germany UK(7.87%) (11.47%) (9.24%) (8.75%) (9.68%) (9.71%)

5 France Spain Germany Italy Japan Italy(7.33%) (6.04%) (8.98%) (7.63%) (8.60%) (9.38%)

6 Spain Japan Italy Portugal Spain Netherlands(2.89%) (5.21%) (6.77%) (3.79%) (5.88%) (4.89%)

7 Netherlands Austria Belgium Austria Belgium Spain(2.67%) (5.06%) (3.81%) (2.67%) (4.78%) (4.12%)

8 Austria Belgium Netherlands Netherlands Netherlands Austria(2.57%) (2.92%) (2.79%) (2.54%) (3.36%) (3.77%)

9 Japan Netherlands Austria Sweden Austria Belgium(2.51%) (2.76%) (1.95%) (2.52%) (2.65%) (3.41%)

10 Belgium Greece Japan Belgium Sweden Denmark(2.17%) (2.66%) (1.71%) (2.36%) (2.23%) (3.16%)

Table 16. Main Competitors in 2005: Goods

Table 17. Main Competitors in 2004: Services

Notes: Percentage points refer to the share of competition coming from each country.

Notes: Percentage points refer to the share of competition coming from each country.

31

Differentiated ULC

Sample of 38 countries



1 Australia Yes Yes Yes2 Austria Yes Yes Yes3 Belgium Yes Yes Yes4 Canada Yes Yes No5 China, P.R.: Hong Kong Yes Yes No6 Colombia Yes No No7 Czech Republic Yes No Yes8 Denmark Yes Yes Yes9 Finland Yes Yes Yes

10 France Yes Yes Yes11 Germany Yes Yes Yes12 Greece Yes Yes Yes13 Hungary Yes No Yes14 Iceland Yes No No15 Ireland Yes Yes Yes16 Israel Yes Yes No17 Italy Yes Yes Yes18 Japan Yes Yes Yes19 Korea Yes Yes No20 Luxembourg Yes Yes Yes21 Macedonia, FYR Yes No No22 Mexico Yes No No23 Netherlands Yes Yes Yes24 New Zealand Yes Yes No25 Norway Yes Yes No26 Poland Yes No Yes27 Portugal Yes Yes Yes28 Singapore Yes Yes No29 Slovak Republic Yes No Yes30 Slovenia Yes No Yes31 South Africa Yes No No32 Spain Yes Yes Yes33 Sweden Yes Yes Yes34 Switzerland Yes Yes No35 Taiwan Yes Yes No36 Turkey Yes No No37 United Kingdom Yes Yes Yes38 United States Yes Yes Yes39 Cyprus No No Yes40 Estonia No No Yes41 Lithuania No No Yes42 Litva No No Yes43 Malta No No Yes

Appendix Table A1. Availability of Unit Labor Cost Data

Country-level ULC

Notes: Country-level ULC data compiled from the OECD database and IMF World Economic Outlookdatabase. Differentiated 2-digit sector level ULC data compiled from EU KLEMS.

Country

32

ISIC Rev.3 Activity Description IMF(4-digit) Conservative Liberal

0111 Growing of cereals and other crops n.e.c. Global Global Global

0112 Growing of vegetables, horticultural specialties and nursery products / / Global

0113 Growing of fruit, nuts, beverage and spice crops Global Global Global

0121 Farming (cattle, sheep, goats, horses, asses, mules and hinnies; dairy) Global Global Global

0122 Other animal farming; production of animal products n.e.c. / / Global

0200 Forestry, logging and related service activities / / Global

0500 Fishing operations / / Global

1110 Extraction of crude petroleum and natural gas / Global /

1200 Mining of uranium and thorium ores / Global Global

1310 Mining of iron ores Global Global Global

1320 Mining of non-ferrous metal ores, except uranium and thorium ores / Global Global

1410 Quarrying of stone, sand and clay / / Global

1421 Mining of chemical and fertilizer minerals / / Global

1422 Extraction of salt / / Global

1429 Other mining and quarrying n.e.c. / / Global

1511 Production, processing and preserving of meat and meat products Global Global Global

1512 Processing and preserving of fish and fish products / / Global

1513 Processing and preserving of fruit and vegetables / / Global

1514 Manufacture of vegetable and animal oils and fats Global Global Global

1520 Manufacture of dairy products / Global Global

1531 Manufacture of grain mill products / / Global

1532 Manufacture of starches and starch products / / Global

1533 Manufacture of prepared animal feeds / / Global

1541 Manufacture of bakery products / / Global

1542 Manufacture of sugar Global Global Global

1543 Manufacture of cocoa, chocolate and sugar confectionery / / Global

1544 Manufacture of farinaceous products (macaroni and similar) / / Global

1549 Manufacture of other food products n.e.c. / / Global

1551 Distilling, rectifying and blending of spirits / / Global

1552 Manufacture of wines / / Global

1553 Manufacture of malt liquors and malt / / Global

1554 Manufacture of soft drinks; production of mineral waters / / Global

1600 Manufacture of tobacco products / / Global

2010 Sawmilling and planing of wood / / Global

2411 Manufacture of basic chemicals, exc. fertilizers & nitrogen compounds Global Global /

2412 Manufacture of basic precious and non-ferrous metals / Global /

2720 Manufacture of basic precious and non-ferrous metals Global Global Global

9302 Hairdressing and other beauty treatment / / Global

Total 9 14 35

Notes: We refer to globally traded goods as goods whose prices are quoted on organized world exchanges as defined by Rauch (1999). These goods arecharacterized as commodities for which a more appropriate definition of market is at the world level. Rauch distinguishes between conservative andliberal classifications. We use the liberal classification that maximizes the number of global goods. For comparison reasons, we identify an additionallist of global goods (denoted by IMF) in line with Bayoumi et al. (2005).

Appendix Table A2. Classification of Global Goods

Rauch Classification

33

34

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